We see it being used in a variety of places. So for instance in the retail
space we've seen retailers take their image catalogues and use the vision
APIs to search for particular attributes of the clothing in the
catalog. So for instance they might be interested in showing a customer just
the green dresses in the catalog or the brown socks for instance. In other cases
we've seeing our customers use the the natural language processing ability of
the cloud to listen to a conversation and then convert that into text that can
be consumed by an application for engaging with customers and customer
care scenarios. And we've seen energy retailers take energy consumption data
and use AI machine learning to apply to that data to advise customers on you
know how their consumption of energy compares to other similar customers in
the area. So we see a lot of space for innovation we see a lot of customers
taking existing data sets and then applying machine learning to pull
further insights and further analytics out of those data sets to drive business
outcomes.
For one of the big four banks that I've worked with we have used AI
and machine learning just to be able to automate and repeated work that our
human workforce used to do. For example a loan officer or a credit assessor would
actually go through a large amount of documentation in order to understand
whether the customer that you being credited to is a good customer and also
what's an ideal customer would look like, and also the next best conversation you
can have with customer. All of that experience when a machine learning like
capabilities were available to the creditor they were able to much better
service the customer and the customer was I would say in a way delighted with
that experience. As opposed to taking the human element completely away. So in
a way you can actually scale a little bit forward as we go across but AI as a
big technology you just want to don't and drop it in the bank environment and
just run with it. It needs to be hand held, it needs to be advocated, it needs
to be have proper awareness around it so people actually embrace those kind of
technologies.
So what we've seen over the last five to ten years is an evolution in some of
the capabilities that are available to to customers of Google Cloud and some of
the other technology platforms out there. Cloud enables customers to tap into huge
amounts of CPU cycles very cost-effectively and also allows them to
tap into cheap and ubiquitous storage. And these are two things that are really
necessary to begin seriously contemplating AI machine learning and
enterprise applications. And then that combined with advances in the state of
the art and things like neural network technologies and other two forms of AI
machine learning mean that enterprises of any kind can begin to entertain the
notion of doing advanced analytics on data that they've otherwise not being
able to tap into for insights.



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